Update app.py

app.py

@@ -1,120 +1,126 @@
-from fastapi import FastAPI, Query
-from fastapi.middleware.cors import CORSMiddleware
-import pandas as pd
-
-from filtered_search_engine import SmartRecommender
-from reranker import Reranker
-from intent_classifier import IntentClassifier
-from keyword_boosting_layer import apply_keyword_boost
-
-# ------------------------------
-# Initialize App
-# ------------------------------
-app = FastAPI(
-    title="Salahkar AI Recommender",
-    description="Smart cultural, heritage & food recommendation engine for BharatVerse",
-    version="1.0.0"
-)
-
-from fastapi.staticfiles import StaticFiles
-
-# CORS Support (allows frontend browser access)
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],  
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# Mount images folder to serve static files
-app.mount("/images", StaticFiles(directory="images"), name="images")
-
-# ------------------------------
-# Load Core Components Once
-# ------------------------------
-print("📌 Loading dataset...")
-df = pd.read_csv("salahkar_enhanced.csv")
-
-print("📌 Loading smart recommendation engine...")
-engine = SmartRecommender()
-
-print("📌 Loading reranker model...")
-reranker = Reranker()
-
-print("📌 Loading intent recognizer...")
-intent_detector = IntentClassifier()
-
-print("🚀 Salahkar AI Ready!")
-
-
-# ------------------------------
-# Routes
-# ------------------------------
-
-@app.get("/")
-def root():
-    return {
-        "message": "🇮🇳 Welcome to Salahkar AI – BharatVerse Intelligent Recommendation System",
-        "usage": "/recommend?query=your text"
-    }
-
-
-@app.get("/recommend")
-def get_recommendation(query: str = Query(..., description="User's search text"), k: int = 7):
-
-    print(f"\n🔍 User Query: {query}")
-
-    # 1️⃣ Detect intent
-    detected_intent = intent_detector.predict_intent(query)
-    print(f"🧠 Intent Detected: {detected_intent}")
-
-    # 2️⃣ FAISS + Filter Search
-    results = engine.recommend(query, k=k)
-
-    # 3️⃣ Prepare results for reranker
-    prepared = []
-    for name, domain, category, region, score in results:
-        row = df[df["name"] == name].iloc[0]
-        prepared.append({
-            "name": name,
-            "domain": domain,
-            "category": category,
-            "region": region,
-            "embedding_score": float(score),
-            "text": row["search_embedding_text"],
-            "image": row["image_file"]
-        })
-
-    # 4️⃣ Re-rank using cross encoder
-    reranked_results = reranker.rerank(query, prepared)
-
-    # 5️⃣ Apply keyword boosting
-    final_results = apply_keyword_boost(query, reranked_results)
-
-    # 6️⃣ Format response for frontend
-    response = [
-        {
-            "name": item["name"],
-            "category": item["category"],
-            "domain": item["domain"],
-            "region": item["region"],
-            "score": float(item["final_score"]),
-            "image": f"/images/{item['image']}" if item.get("image") else None
-        }
-        for item in final_results[:k]
-    ]
-
-    return {
-        "query": query,
-        "intent": detected_intent,
-        "results": response
-    }
-
-
-# -------------------------------------------
-# Run (Ignored by HuggingFace — needed only for local testing)
-# -------------------------------------------
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+from fastapi import FastAPI, Query
+from fastapi.middleware.cors import CORSMiddleware
+import pandas as pd
+
+from filtered_search_engine import SmartRecommender
+from reranker import Reranker
+from intent_classifier import IntentClassifier
+from keyword_boosting_layer import apply_keyword_boost
+
+# ------------------------------
+# Initialize App
+# ------------------------------
+app = FastAPI(
+    title="Salahkar AI Recommender",
+    description="Smart cultural, heritage & food recommendation engine for BharatVerse",
+    version="1.0.0"
+)
+
+from fastapi.staticfiles import StaticFiles
+
+# CORS Support (allows frontend browser access)
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Mount images folder to serve static files
+app.mount("/images", StaticFiles(directory="images"), name="images")
+
+# ------------------------------
+# Load Core Components Once
+# ------------------------------
+print("📌 Loading dataset...")
+df = pd.read_csv("salahkar_enhanced.csv")
+
+print("📌 Loading smart recommendation engine...")
+engine = SmartRecommender()
+
+print("📌 Loading reranker model...")
+reranker = Reranker()
+
+print("📌 Loading intent recognizer...")
+intent_detector = IntentClassifier()
+
+print("🚀 Salahkar AI Ready!")
+
+
+# ------------------------------
+# Routes
+# ------------------------------
+
+@app.get("/")
+def root():
+    return {
+        "message": "🇮🇳 Welcome to Salahkar AI – BharatVerse Intelligent Recommendation System",
+        "usage": "/recommend?query=your text"
+    }
+
+
+@app.get("/recommend")
+def get_recommendation(query: str = Query(..., description="User's search text"), k: int = 7):
+
+    print(f"\n🔍 User Query: {query}")
+
+    # 1️⃣ Detect intent
+    detected_intent = intent_detector.predict_intent(query)
+    print(f"🧠 Intent Detected: {detected_intent}")
+
+    # 2️⃣ FAISS + Filter Search
+    # engine.recommend returns (results_list, intent)
+    rec_results, _ = engine.recommend(query, k=k)
+
+    # 3️⃣ Prepare results for reranker
+    prepared = []
+    for item in rec_results:
+        name = item["name"]
+        domain = item["domain"]
+        category = item["category"]
+        region = item["region"]
+        score = item["score"]
+        row = df[df["name"] == name].iloc[0]
+        prepared.append({
+            "name": name,
+            "domain": domain,
+            "category": category,
+            "region": region,
+            "embedding_score": float(score),
+            "text": row["search_embedding_text"],
+            "image": row["image_file"]
+        })
+
+    # 4️⃣ Re-rank using cross encoder
+    reranked_results = reranker.rerank(query, prepared)
+
+    # 5️⃣ Apply keyword boosting
+    final_results = apply_keyword_boost(query, reranked_results)
+
+    # 6️⃣ Format response for frontend
+    response = [
+        {
+            "name": item["name"],
+            "category": item["category"],
+            "domain": item["domain"],
+            "region": item["region"],
+            "score": float(item["final_score"]),
+            "image": f"/images/{item['image']}" if item.get("image") else None
+        }
+        for item in final_results[:k]
+    ]
+
+    return {
+        "query": query,
+        "intent": detected_intent,
+        "results": response
+    }
+
+
+# -------------------------------------------
+# Run (Ignored by HuggingFace — needed only for local testing)
+# -------------------------------------------
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)